Richard Jones, VP Sales Northern Europe, Confluent
Developing a 360-degree understanding of your customers is complicated. No matter the industry, you need the permission and the technology to collect swathes of personal data in almost real-time, and then the ability to apply that data to the customer experience.
Despite that complexity, customer expectations are rising. With financial services increasingly associated with digital transformation, as brick-and-mortar branches continue to close and digital disruptors like Starling receive acclaim, customers want a slick digital experience. They want quotes to take hours or even minutes, rather than days – and for their personal data to be reflected in their journey.
Unfortunately, it’s difficult to meet those expectations. The data that feeds a customer profile is often incomplete, and there are human links at every critical point in the underwriting chain. You can’t sacrifice the human element, as manual intervention – and interacting with someone you feel you can trust – is a key part of underwriting. But manual processes will inevitably slow the process down.
Customer satisfaction, as a result, is falling. The British Association of Insurers has found that just 13% of us give our insurance providers an 8/10 or higher when it comes to trust; and 70% believe that premiums go up no matter what they do, suggesting their insurer isn’t really interested in their personal circumstances.
That’s where artificial intelligence (AI) can help. We’re not just talking about introducing new technologies to underwriters, but more about a ‘data transformation.’ To meet customer expectations at the required speed, you need a system that can process an immense volume of data in real time and use that to shape the customer journey across the board.
But as we’ve said, you can’t abandon that human touch entirely. Research from McKinsey specifically calls out “employee courtesy” and “employee knowledge and professionalism” as fundamental to customer satisfaction.
So, how do we balance these two things? How do we automate enough of the underwriting process without becoming too mechanical?
Data-driven differentiation
Ultimately, underwriting – and all insurance – is about your organisation’s ability to quantify risk. The more accurate, high-value, timely data you have at your fingertips, the more likely you are to accurately assess the risk that a certain customer represents. This isn’t lost on underwriters either, who recognise the need to fuel the systems that will bring customers the experience they crave.
PwC has suggested in recent research that a huge 97% of London Market insurers see the ability to “better leverage business data” as a driver for technological transformation. While Deloitte actually finds that underwriting organisations are one of the more mature in financial services when it comes to analytics, with 72% of the surveyed organisations categorised as either ‘adopter’ or ‘pioneer.’
They’re doing so to address the need to reduce the time between data entering the system and it being available to business-critical systems. And that data can’t be siloed, either – it needs to be accessible to the divergent teams right across the organisation.
In simple terms, insurers are only as good as their data. The more accurate, the faster, and the higher quality that data is, the better products and services they can offer.
At your own risk
With all that said, insurers are risk-averse – as you’d expect. Data transformation will inevitably render legacy tech obsolete, and removing such systems from an organisation as interconnected and nuanced as an underwriter is no mean feat.
As a result, it’s often convenient to push back on upgrading such technology. The process of replacing it can be long and expensive, and there’s no guarantee that their long-term strategic objectives might change.
There’s also the logistical complexity to consider. Any system that seeks to de-silo data and make it accessible needs to integrate business-wide, which demands an intensive audit. And the removal of systems that people are used to demands training, which costs both initial investment and the loss of some productivity while staff get back up to speed.
Many underwriters are caught, then, between an inability to meet customer expectations compared to their data-driven rivals, and ‘biting the bullet’ on their own data transformation. But as failing to adapt will ultimately render them obsolete, that point of change has to come sooner or later.
Now it’s personal
That covers the technology – but what, as we’ve said, about balancing it with that human touch?
Well, data capture isn’t just about the financial aspects of a customer. Every customer data point – from an address history to customer service records – should impact how that person is treated.
In time, that data helps to build a comprehensive customer profile that can act as a ‘single source of truth’ for that person no matter which system or staff member is handling them. It’s the convergence point of every data stream, and that makes it the one-stop touchpoint for informing a customer’s experience.
That can drastically accelerate the process that a staff member can provide, without sacrificing the personability that is so important in the first place. Being able to refer to customer records, direct them to an offer on their favourite device, or using a preferred name in email comms, gives them the feeling of being recognised as a valued customer.
This sort of ‘hyper-personalisation’ is a powerful weapon to combat falling customer sentiment. If you can prove that you understand an individual and can use that understanding to pull them through a quick and efficient customer experience, there’s little room for dissatisfaction.
Ultimately, that balance is the goal for modern underwriters. To move at the speed of digital disruptors, underwriters have to be able to transform their systems to prioritise data, without compromising that human touch. If they can do so, they differentiate themselves from the competition in the best way possible – and prime themselves to take advantage of new technologies in the future.
